Systems and methods for generating vehicle insurance policy data based on empirical vehicle related data

ABSTRACT

The present disclosure generally relates to a computer implemented system and method for automatically generating insurance policy related data. The system and method may determine a vehicle operator and generate empirical vehicle operator identity data. The system and method may further acquire empirical vehicle operation data related to the actual vehicle operator, and correlate the empirical vehicle operator identity data and the empirical vehicle operation data to generate vehicle insurance policy related data. The system and method may further include processing one or more insurance options, including underwriting and pricing, based at least in part on the vehicle insurance policy related data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/203,349, filed on Mar. 10, 2014, which claims the benefit of U.S.Provisional Application No. 61/775,652, filed on Mar. 10, 2013. Thedisclosure of each of which is incorporated by reference herein in itsentirety.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forassessing, pricing, and provisioning vehicle insurance. In particular,the present disclosure relates to systems and methods for generatingvehicle insurance policy data based on empirical vehicle operatoridentity data and empirical vehicle operation data.

BACKGROUND

Vehicle insurance policies may be based, at least in part, oninformation related to a vehicle insurance policy applicant, such as ageof the applicant, gender of the applicant, number of prior insuranceclaim(s) that the applicant has submitted, driving record of theapplicant, etc. Vehicle insurance policies may also be based, at leastin part, on information related to a driving routine associated with thevehicle insurance policy applicant, such as where the insuranceapplicant lives and where the applicant drives to work.

Various sensors, such as seat belt sensors, seat occupancy sensors,vehicle telematics sensors, infrared sensors, vibration sensors, imagesensors, ultrasonic sensors, etc., are being incorporated withinmodern-day vehicles. Data derived from associated sensors is used tomonitor and/or control vehicle operation.

SUMMARY

Generating vehicle insurance policy related data based on empiricalvehicle related data is desirable. In particular, it is desirable toautomatically generate insurance policy related data based on empiricaldata related to a vehicle operator identity and/or empirical datarelated to vehicle operation.

A computer implemented method for automatically generating insurancepolicy data, that is representative of a vehicle insurance policy, mayinclude receiving, at one or more processors, empirical vehicle operatoridentity data that may be representative of an identity of a vehicleoperator. The method may further include receiving, at one or moreprocessors, empirical vehicle operation data that may be representativeof actual operation of a vehicle and that may be, at least partially,based on vehicle sensor data. The method may also include correlating,by one or more processors, at least a portion of the empirical vehicleoperator identity data with at least a portion of the empirical vehicleoperation data. The method may yet further include generating, by one ormore processors, vehicle insurance policy related data based, at leastin part, on the correlated empirical vehicle operator identity data andempirical vehicle operation data.

In an embodiment, a system for automatically generating vehicleinsurance policy related data, that is representative of a vehicleinsurance policy, may include an empirical vehicle operator identitydata acquisition module stored on a memory that, when executed by aprocessor, causes the processor to acquire empirical vehicle operatoridentity data that may be representative of an identity of a vehicleoperator. The system may also include an empirical vehicle operationdata acquisition module stored on a memory that, when executed by aprocessor, causes the processor to acquire empirical vehicle operationdata that may be representative of operation of a vehicle. The systemmay further include a vehicle insurance policy data generation modulestored on a memory that, when executed by a processor, causes theprocessor to generate vehicle insurance policy related data based, atleast in part, on the empirical vehicle operator identity data and theempirical vehicle operation data.

In another embodiment, a tangible, computer-readable medium may storeinstructions that, when executed by a process, cause the processor toautomatically generate vehicle insurance policy related data that isrepresentative of a vehicle insurance policy. The tangible,computer-readable medium may also include an empirical vehicle operatoridentity data acquisition module that, when executed by a processor,causes the processor to acquire empirical vehicle operator identity datathat may be representative of an identity of a vehicle operator. Thetangible, computer-readable medium may further include an empiricalvehicle operation data acquisition module that, when executed by aprocessor, causes the processor to acquire empirical vehicle operationdata that may be representative of operation of a vehicle. The tangible,computer-readable medium may also include a vehicle insurance policydata generation module that, when executed by a processor, causes theprocessor to generate vehicle insurance policy related data based, atleast in part, on the empirical vehicle operator identity data and theempirical vehicle operation data.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the systems andmethods disclosed herein. It should be understood that each figuredepicts an embodiment of a particular aspect of the disclosed systemsand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 illustrates a block diagram of a computer system on which anexemplary vehicle insurance policy data generation system and method mayoperate in accordance with the described embodiments;

FIGS. 2A-2C depict various views of the interior of an example vehiclethat illustrate locations of vehicle sensors within, and on, a vehicle;

FIGS. 3A-3C illustrate various example images constructed from dataretrieved from the vehicle devices of FIGS. 2A-2C;

FIG. 4 illustrates a block diagram of an exemplary vehicle module foruse in generating and transmitting empirical vehicle operator identitydata and empirical vehicle operation data;

FIG. 5 depicts a flow diagram of an example method of generating andtransmitting empirical vehicle operator identity data;

FIG. 6 depicts a flow diagram of an example method of generating andtransmitting empirical vehicle operations data;

FIG. 7 illustrates a block diagram of an exemplary remote server for usein receiving empirical vehicle operator identity data and empiricalvehicle operations data, and generating vehicle insurance policy databased on the empirical vehicle operator identity data and empiricalvehicle operations data; and

FIG. 8 depicts a flow diagram of an example method of generating vehicleinsurance policy data based on empirical vehicle operator identity dataand empirical vehicle operations data.

DETAILED DESCRIPTION

While vehicle insurance rates are typically based, at least in part, oninformation associated with an applicant, or applicants, seekinginsurance coverage, undisclosed drivers often operate the associatedvehicle(s). Methods and systems are provided that automatically generatevehicle insurance policy related data based on empirical vehicleoperator identity data. The empirical vehicle operator identity data maybe representative of an identity of an operator, or operators, that haveactually operated an associated insured vehicle. Empirical vehicleoperator identity data may, for example, be based on data acquired fromvarious vehicle sensors, such as seat occupancy sensors, seatbeltsensors, body heat sensors (e.g., infrared sensors), weight sensors(e.g., pressure transducers), cameras (e.g., image sensors), etc. Thevehicle sensor data may be time stamped.

In addition to vehicle insurance policy rates being based on informationpertaining to an insurance applicant, vehicle insurance policy rates maybe based on information related to operation of the vehicle. Forexample, vehicle insurance customers who operate their vehicles for lesstime generally pay a lower amount for vehicle insurance when compared tocustomers who operate their vehicles frequently, all other factors beingequal. In addition to, or as an alternative to, generating vehicleinsurance policy data based on empirical vehicle operator identity data,the present systems and methods may generate vehicle insurance policydata based on empirical vehicle operation data. Empirical vehicleoperation related data may be representative of an amount of time aninsured vehicle was actually in use. For example, travel time may beused as a unit of exposure for, at least in part, determining a vehicleinsurance rate. In particular, a vehicle motion sensor (e.g., a vehiclespeedometer sensor, a vehicle odometer sensor, a vibration sensor or alight sensor) may be used to detect motion of a vehicle. Data receivedfrom a vehicle motion sensor may be time stamped. The time stampedvehicle motion sensor data may be used to generate, record and transmitempirical vehicle operation related data that may be representative of alength of time a vehicle was in use.

Turning to FIG. 1, a high-level block diagram of a vehicle insurancepolicy data generation system 100 is illustrated that may implementcommunications between a vehicle module 105 and a remote computingdevice 110 (e.g., a remote server) to receive vehicle sensor data, togenerate empirical vehicle operator identity data, to generate empiricalvehicle operation data and to generate vehicle insurance policy data.For example, the vehicle insurance policy data generation system 100 mayacquire data from vehicle sensors (e.g., vehicle telematics systemssensors, seat belt sensors, steering wheel sensors, seat occupancysensors, vibration sensors, image sensors, infrared sensors ultrasonicsensors, audio sensors, pressure sensors, etc.) and generate empiricalvehicle operator identity data, empirical vehicle operation data andvehicle insurance policy data based on the vehicle sensor data. Thesevehicle sensors may, for example, be located as denoted with regard toreference numbers 225 a, 235 a, 245 a, 260 a, 280 b of FIGS. 2A and 2B.

For clarity, only one vehicle module 105 is depicted in FIG. 1. WhileFIG. 1 depicts only one vehicle module 105, it should be understood thatany number of vehicle modules 105 may be supported. The vehicle module105 may include a memory 120 and a processor 125 for storing andexecuting, respectively, a module 121. The module 121, stored in thememory 120 as a set of computer-readable instructions, may be related toan empirical vehicle operator identity data module (e.g., empiricalvehicle operator identity data module 421 of FIG. 4) and/or an empiricalvehicle operation data module (e.g., empirical vehicle operation datamodule 422 of FIG. 4). Execution of the module 121 may also cause theprocess 125 to associate the empirical vehicle operator identity dataand/or the empirical vehicle operation data with a time and, or date(i.e., “time stamp” the data). Execution of the module 121 may alsocause the processor 125 to receive known vehicle operator identity datafrom, for example, an insurance related database (e.g., insurancerelated database 170 of FIG. 1). Execution of the module 121 may furthercause the processor 125 to communicate with the processor 155 of theremote computing device 110 via the network interface 130, the vehiclemodule communications network connection 131 and the wirelesscommunication network 115 to transmit empirical vehicle operatoridentity data and/or the empirical vehicle operation data from thevehicle module 105 to the remote server 110.

The vehicle module 105 may further include an image sensor input 135communicatively connected to a first image sensor 136 and a second imagesensor 137. While two image sensors 136, 137 are depicted in FIG. 1, anynumber of image sensors may be included. The vehicle module 105 may alsoinclude an infrared sensor input 140 communicatively connected to afirst infrared sensor 141 and a second infrared sensor 142. While twoinfrared sensors 141, 142 are depicted in FIG. 1, any number of infraredsensors may be included. The vehicle module 105 may further include anultrasonic sensor input 145 communicatively connected to a firstultrasonic sensor 146 and a second ultrasonic sensor 147. While twoultrasonic sensors 146, 147 are depicted in FIG. 1, any number ofultrasonic sensors may be included. The vehicle module 105 may alsoinclude a microphone input 150 communicatively connected to a firstmicrophone 151 and a second microphone 152. While two microphones 151,152 are depicted in FIG. 1, any number of microphones may be included.The vehicle module 105 may also include a vibration sensor inputs 106communicatively connected to a first vibration sensor 107 and a secondvibration sensor 108. While two vibration sensors 107, 108 are depictedin FIG. 1, any number of vibration sensors may be included. The vehiclemodule 105 may also include seat occupancy sensor inputs 122communicatively connected to a first seat occupancy sensor 123 and asecond seat occupancy sensor 124. While two seat occupancy sensors 123,124 are depicted in FIG. 1, any number of seat occupancy sensors may beincluded. Any one of the seat occupancy sensors 123, 124 may be, forexample, an ultrasonic sensor, a pressure sensor, a body heat sensor(e.g., an infrared sensor) or a camera/video sensor (e.g., an imagesensor). The vehicle module 105 may also include steering wheel sensorinputs 132 communicatively connected to a first steering wheel sensor133 and a second steering wheel sensor 134. While two steering wheelsensors 133, 134 are depicted in FIG. 1, any number of steering wheelsensors may be included. Any one of the steering wheel sensors 133, 134may be, for example, an ultrasonic sensor, a pressure sensor, a bodyheat sensor (e.g., an infrared sensor) or a camera/video sensor (e.g.,an image sensor). The vehicle module 105 may also include seat beltsensor inputs 127 communicatively connected to a first seat belt sensor128 and a second seat belt sensor 129. While two seat belt sensors 128,129 are depicted in FIG. 1, any number of seat belt sensors may beincluded. The vehicle module 105 may further include vehicle telematicssystem inputs 126. The vehicle telematics system inputs 126 may include,for example, a global positioning system (GPS) sensor, a vehiclespeedometer sensor, a vehicle odometer sensor, a vehicle air bag sensor,a vehicle interior temperature sensor, a vehicle exterior temperaturesensor, a vehicle pitch sensor, a vehicle yaw sensor and/or a time andday clock sensor. The vehicle module 105 may further include adisplay/user input device 125.

As one example, a first image sensor 136 may be located in a driver-sideA-pillar (e.g., location of vehicle sensor 235 a of FIG. 2A), a secondimage sensor 137 may be located in a passenger-side A-pillar (e.g.,location of vehicle sensor 245 a of FIG. 2A), a first infrared sensor141 may be located in a driver-side B-pillar (e.g., location of vehiclesensor 280 b of FIG. 2B), a second infrared sensor 142 may be located ina passenger-side B-pillar (not shown in the Figs.), first and secondultrasonic sensors 146, 147 may be located in a center portion of avehicle dash (e.g., location of vehicle sensor 225 a of FIG. 2A) andfirst and second microphones 151, 152 may be located on a bottom portionof a vehicle interior rearview mirror (e.g., location of vehicle sensor260 a of FIG. 2A). The processor 115 may acquire vehicle sensor datafrom any one of, or all of, these vehicle sensors 107, 108, 123, 124,126, 128, 129, 133, 134, 136, 137, 141, 142, 146, 147, 151, 152 and maygenerate real-time vehicle operator identity data, empirical vehicleoperator identity data and/or empirical vehicle operation data based onthe vehicle sensor data. The processor 115 may transmit empiricalvehicle operator identity data and/or empirical vehicle operation datato the remote computing device 110. Alternatively, the processor 115 maytransmit vehicle sensor data and/or real-time vehicle operator identitydata to the remote computing device 110 and the processor 155 maygenerate empirical vehicle operator identity data and/or empiricalvehicle operation data based on the vehicle sensor data and/or real-timevehicle operator identity data.

The network interface 130 may be configured to facilitate communicationsbetween the vehicle module 105 and the remote computing device 110 viaany hardwired or wireless communication network 115, including forexample a wireless LAN, MAN or WAN, WiFi, the Internet, a Bluetoothconnection, or any combination thereof. Moreover, the vehicle module 105may be communicatively connected to the remote computing device 110 viaany suitable communication system, such as via any publicly available orprivately owned communication network, including those that use wirelesscommunication structures, such as wireless communication networks,including for example, wireless LANs and WANs, satellite and cellulartelephone communication systems, etc. The vehicle module 105 may causeinsurance risk related data to be stored in a remote computing device110 memory 160 and/or a remote insurance related database 170.

The remote computing device 110 may include a memory 160 and a processor155 for storing and executing, respectively, a module 161. The module161, stored in the memory 160 as a set of computer-readableinstructions, facilitates applications related to generation of vehicleinsurance policy data. The module 161 may also facilitate communicationsbetween the computing device 110 and the vehicle module 105 via anetwork interface 165, a remote computing device network connection 166and the network 115 and other functions and instructions.

The computing device 110 may be communicatively coupled to an insurancerelated database 170. While the insurance related database 170 is shownin FIG. 1 as being communicatively coupled to the remote computingdevice 110, it should be understood that the insurance related database170 may be located within separate remote servers (or any other suitablecomputing devices) communicatively coupled to the remote computingdevice 110. Optionally, portions of insurance related database 170 maybe associated with memory modules that are separate from one another,such as a memory 120 of the vehicle module 105. The processor 155 mayfurther execute the module 161 to store known vehicle operator identitydata within the insurance related database 170. The known vehicleoperator identity data may be generated based on digital images ofindividuals associated with an insurance policy application and/or otherauthorized drivers associated with an insurance policy application.

Turning to FIGS. 2A-2C, vehicle sensor systems 200 a, 200 b, 200 c areillustrated. As depicted in FIG. 2A, the vehicle sensor system 200 a mayinclude a center-dash vehicle sensor 225 a located in a center area ofthe dash, a driver-side A-pillar vehicle sensor 235 a located in adriver side A-pillar 230 a, a passenger-side A-pillar vehicle sensor 245a located in a passenger-side A-pillar 240 a and a rearview mirrorvehicle sensor 260 a located on a bottom-side of the rearview mirror 255a. The vehicle sensor system 200 a may further, or alternatively,include vehicle sensors in a driver-side visor 265 a, a passenger-sidevisor 270 a, a rearview mirror mounting bracket 250 a and, or thesteering wheel 210 a. As described in detail herein, a position of aleft-hand 215 a of a vehicle driver and, or a position of a right-hand220 a of the vehicle driver, relative to a vehicle steering wheel 210 amay be determined based on data acquired from any one of the vehiclesensors 225 a, 235 a, 245 a, 260 a. Any one of the vehicle sensors 225a, 235 a, 245 a, 260 a may be an image sensor 136, 137, a pressuresensor 123, 124, a vibration sensor 107, 108, an infrared sensor 141,142, an ultrasonic sensor 145, 146, a microphone 151, 152 or any othersuitable vehicle sensor. Empirical vehicle operator identity data,real-time vehicle operator identity data and/or empirical vehicleoperation data may be generated based on data received from any one of,or any combination of, vehicle sensors shown in FIG. 2A.

With reference to FIG. 2B, the vehicle sensor system 200 b may include adriver-side B-pillar vehicle sensor 280 b located in a driver-sideB-pillar 275 b and a center-dash vehicle sensor 225 b located in acenter area of the dash. While not shown in FIG. 2B, the vehicle sensorsystem 200 b may include a passenger-side B-pillar vehicle sensor and,or any other vehicle sensors as described in conjunction with FIG. 2A.The vehicle sensor system 200 b may further include a display device 285b. The display device 285 b may be located in a center-console area. Asillustrated in FIG. 2B, data acquired from the vehicle sensors 225 b,280 b may be used to determine an identity of an occupant of adriver-side seat 290 b, a passenger-side seat 295 b, a position of handson a steering wheel 210 b and, or at least a portion of a face of avehicle driver (not shown in FIG. 2B). Empirical vehicle operatoridentity data, real-time vehicle operator identity data and/or empiricalvehicle operation data may be generated based on data received from anyone of, or any combination of, vehicle sensors shown in FIG. 2B.

Turning to FIG. 2C, the vehicle sensor system 200 c may include adriver-side A-pillar vehicle sensor 235 c located in a driver sideA-pillar 230 c, a passenger-side A-pillar vehicle sensor 245 c locatedin a passenger-side A-pillar 240 c and a rearview mirror vehicle sensor260 c located on a bottom-side of the rearview mirror 255 c. The vehiclesensor system 200 c may further, or alternatively, include vehiclesensors in a rearview mirror mounting bracket 250 c and, or the steeringwheel 210 c. While not shown in FIG. 2C, the vehicle monitoring system200 c may include any other vehicle sensors as described in conjunctionwith FIGS. 2A and 2B. As illustrated in FIG. 2C, data acquired from thevehicle sensors 235 c, 245 c may be used to generate vehicle operatoridentity data corresponding to an occupant of a driver-side seat 290 c,a passenger-side seat 295 c occupancy, a position of hands on a steeringwheel 210 c and, or at least a portion of a face of a vehicle driver(not shown in FIG. 2C). Driver position within the driver-side seat 290c may, for example, be inferred from shifting weight on the seat 290 c.Shifting weight on a seat may be determined via a signal obtained from apressure transducer 123, 124 located with the seat. Empirical vehicleoperator identity data, real-time vehicle operator identity data and/orempirical vehicle operation data may be generated based on data receivedfrom any one of, or any combination of, vehicle sensors shown in FIG.2C.

With reference to FIGS. 3A-3C, vehicle interiors 300 a, 300 b, 300 c aredepicted. As described in detail herein, data acquired from the vehiclesensors 325 a, 335 a, 345 a, 360 a, 380 b of FIGS. 3A and 3B (or anyother suitably located vehicle sensors) may be used to determine aposition of at least a portion of a passenger 397 a within the vehicleinterior 300 a. The data acquired from the vehicle sensors 325 a, 335 a,345 a, 360 a, 380 b (or any other suitably located vehicle sensors) maybe used to determine whether, or not the passenger 397 a is wearing aseatbelt 396 a. As further illustrated in FIG. 3A, data acquired fromthe vehicle sensors 325 a, 335 a, 345 a, 360 a, 380 b of FIGS. 3A and 3B(or any other suitably located vehicle sensors) may be used to determinea position and, or orientation of a vehicle driver's head 319 a and, orright-hand 320 a on a steering wheel 310 a. For example, the dataacquired from the vehicle sensors 325 a, 335 a, 345 a, 360 a, 380 b maybe used to determine whether the vehicle driver's head 319 a is orientedtoward a rearview mirror 355 a, oriented toward the driver-side A-pillar330 a or oriented toward the front windshield. The data acquired fromthe vehicle sensors 325 a, 335 a, 345 a, 360 a, 380 b may be used todetermine whether the driver is wearing a seatbelt 391 a. In any event,the vehicle interior 300 a may include a microphone 350 a locatedproximate the rearview mirror 355 a. As described in detail herein, dataacquired from the microphone 350 a may be used to determine a source ofsound within and/or around the vehicle 300 a and, or a volume of thesound. Empirical vehicle operator identity data, real-time vehicleoperator identity data and/or empirical vehicle operation data may begenerated based on data received from any one of, or any combination of,vehicle sensors shown in FIG. 3A.

FIG. 3B depicts a vehicle interior 300 b including a driver-sideA-pillar vehicle sensor 335 b located on a driver-side A-pillar 330 b.As described in detail herein, data acquired from the vehicle sensor 335b (along with any other suitably located vehicle sensors) may be used todetermine a position and, or orientation of a driver's head 319 b, thedriver's left hand 315 b and, or right hand 320 b relative to thesteering wheel 310 b. For example, data acquired from the vehicle sensor335 b (along with any other suitably located vehicle sensors) may beused to determine a gesture that the driver is performing with her lefthand 315 b. Empirical vehicle operator identity data, real-time vehicleoperator identity data and/or empirical vehicle operation data may begenerated based on data received from any one of, or any combination of,vehicle sensors shown in FIG. 3B. For example, data from a microphone151, 152 may be used to identify a vehicle operator and/or a number ofoccupants. In particular, a number of distinct voices may be determinedbased on data from a microphone 151, 152. Alternatively, oradditionally, the number of door opening and closing sounds may bedetermined based on data from a microphone 151, 152. Furthermore, datafrom a microphone 151, 152 may be also be used to identify a particularvehicle.

Turning to FIG. 3C, a vehicle interior 300 b depicts a vehicle sensor360 c located on a bottom side of a rearview mirror 355 c opposite arearview mirror mount 350 c. As described in detail herein, dataacquired from the vehicle sensor 360 c (along with any other suitablylocated vehicle sensors) may be used to determine a position and, ororientation of a driver's head 319 c, the driver's left hand 315 c and,or right hand 320 c relative to the steering wheel 310 c. For example,data acquired from the vehicle sensor 360 c (along with any othersuitably located vehicle sensors) may be used to determine that thedriver's head 319 c is oriented toward a cellular telephone 321 c in herright hand 320 c. Alternatively, or additionally, data acquired from theposition sensor 360 c (along with any other suitably located vehiclesensors) may be used to determine the presence of a cell phone in adriver's hand. As also described in detail herein, a determination maybe made that the driver is inattentive to the road based on the driver'shead 319 c being oriented toward the cellular telephone 321 c. Empiricalvehicle operator identity data, real-time vehicle operator identity dataand/or empirical vehicle operation data may be generated based on datareceived from any one of, or any combination of, vehicle sensors shownin FIG. 3C.

Turning to FIGS. 4 and 5, a vehicle module 405 of a vehicle insurancepolicy data generation system 400 is depicted along with a method ofgenerating empirical vehicle operator identity data on the vehiclemodule 405 and, or transmitting the empirical vehicle operator identitydata to a remote server 110. The vehicle module 405 may be similar tothe vehicle module 121 of FIG. 1. The method 500 may be implemented byexecuting the modules 421, 424 on a processor (e.g., processor 115).

In any event, the vehicle module 405 may include an empirical vehicleoperator identity data acquisition module 421 and an empirical vehiclerelated data transmission module 424 stored on a memory 420. Theprocessor 115 may store a vehicle insurance application module on amemory (e.g., memory 420) of the vehicle module 405 and the vehicleinsurance application module may be configured (block 505). Theprocessor 115 may execute the empirical vehicle operator identity dataacquisition module 421 and cause the processor 115 to acquire vehicleoperator identity sensor data from at least one vehicle sensor (block510). The processor 115 may further execute the empirical vehicleoperator identity data acquisition module 421 and cause the processor115 to generate real-time vehicle operator identity data (block 510).The processor 115 may further execute the empirical vehicle operatoridentity data acquisition module 421 and cause the processor 115 toreceive known vehicle operator identity data (block 510). The processor115 may further execute the empirical vehicle operator identity dataacquisition module 421 and cause the processor 115 to generate empiricaloperator identity data based on, for example, a comparison of thereal-time vehicle operator identity data with the known vehicle operatoridentity data (block 515). The processor 115 may execute the empiricalvehicle related data transmission module 424 to cause the processor 115to transmit the empirical vehicle operator identity data to a remoteserver (e.g., remote server 110 of FIG. 1) (block 520).

The method of generating empirical vehicle operator identity data 500may include using a picture and/or a video of a vehicle operator's faceto identify the driver of the vehicle. For example, the method 500 mayinclude capturing at least one image of each person who is authorized tooperate a vehicle in accordance with an associated insurance policy. Theimages may be stored within a database (e.g., insurance related database170 of FIG. 1) with other empirical vehicle operator identity data. Acamera and/or video device (e.g., image sensor 136, 137 of FIG. 1) maybe provided within an associated insured vehicle. The camera and/orvideo device 136, 137 may, for example, be mounted on the dashboard(e.g., dashboard 225 b of FIG. 2B) or steering wheel (e.g., steeringwheel 210 b of FIG. 2B) of an insured vehicle. The camera and/or videodevice 136, 137 may be activated when a driver occupies the vehicle. Acamera and/or video device 136, 137 may be activated when a driverunlocks and enters the vehicle (e.g., sits in the driver's seat).Alternatively, a camera and/or video device 136, 137 may be activatedwhen the driver grasps the steering wheel 210 b. Optionally, a cameraand/or video device 13, 137 may be activated when the driver inserts thekey in an ignition of the insured vehicle. An image and/or video of thevehicle operator's face may be captured. Empirical vehicle operatoridentity data may be generated based on the captured image or video,using, for example, image recognition technology to identify key facialcharacteristics. The image recognition technology may, for example,determine a physical status of the driver by comparing an image of thedriver, that was captured from within the vehicle, to a series of imagesthat had previously been stored within an associated database 170. Thefacial characteristics data may be used to assess a current physicalstatus of the driver within a predetermined set of rules. Thereby, anindividual may be prohibited from operating a vehicle if the driver isnot authorized to drive the vehicle or an authorized driver isdetermined to be in a condition unsuitable for operating the vehicle.For example, a facial image of a vehicle operator may be comparedagainst a library of known vehicle operators to determine whether thevehicle operator has permission to drive a particular vehicle. If thevehicle operator does not have permission to drive the vehicle, thesystem (e.g., processor 115) may prohibit operation of the vehicle.Alternatively or additionally, if the vehicle operator does not havepermission to drive the vehicle because the operator is not named on aninsurance policy (i.e., the operator is an undisclosed driver), thesystem (e.g., processor 115) may generate an audit function to audit theactual owner/policy holder or otherwise inform him or her of theunauthorized/undisclosed driver.

Further, a facial image of a vehicle operator may be used to determineif the operator is wearing their corrective lenses, if required inaccordance with her driver's license. If the driver is required to wearcorrective lenses and does not have them on, operation of the vehiclemay be prohibited. Yet further, a facial image of a vehicle operator maybe used to determine if the operator is too tired or stressed to operatethe vehicle. Images of faces, even in static photos, may show keycharacteristics of weariness and stress. Weariness and/or stress mayaffect the reflexes and acuity of a vehicle operator and may impact anability of the vehicle operator to drive the vehicle. If a vehicleoperator is determined to be too stressed or tired, the operation of thevehicle may be prohibited. Furthermore, an owner of a vehicle mayrequire authentication of an operator of his vehicle prior to thevehicle being enabled for operation. Moreover, an owner of a vehicle mayrequire validation that an authorized driver is in a suitable conditionto operate the vehicle.

With further reference to FIG. 4, along with reference to FIG. 6, avehicle module 405 of a vehicle insurance policy data generation system400 is depicted along with a method of generating empirical vehicleoperations data on the vehicle module 405 and, or transmitting empiricalvehicle operations data to a remote server 110. The vehicle module 405may be similar to the vehicle module 121 of FIG. 1. The method 600 maybe implemented by executing the modules 422, 424 on a processor (e.g.,processor 115).

In any event, the vehicle module 405 may include an empirical vehicleoperation data acquisition module 422 and an empirical vehicle relateddata transmission module 424. The processor 115 may store a vehicleinsurance application module on a memory (e.g., memory 420) of thevehicle module 405 and the vehicle insurance application module may beconfigured (block 605). The processor 115 may execute the empiricalvehicle operation data acquisition module 422 to cause the processor 115to receive vehicle operation sensor data (block 610). The processor 115may further execute the empirical vehicle operation data acquisitionmodule 422 to cause the processor 115 to generate empirical vehicleoperation data based on the vehicle operation sensor data (block 615).The processor 115 may execute the empirical vehicle related datatransmission module 424 to cause the processor 115 to transmit empiricalvehicle operation data to a remote server (e.g., remote server 110 ofFIG. 1) (block 620).

The processor 115 may execute an empirical vehicle operating environmentdata acquisition module 423 to cause the processor 115 to receivevehicle sensor data associated with an operating environment of thevehicle. For example, the processor 115 may generate empirical vehicleoperating environment data based on data acquire from a temperaturesensor, a rain sensor, an ice sensor, a snow sensor or other vehiclesensor capable of sensing an operating environment associated with thevehicle.

A method of generating empirical vehicle operation related data 600 may,for example, include detecting driving patterns. For example, vehicleoperation data may be received from a vehicle telematics system (e.g., aGPS or a steering wheel angle sensor). Vehicle operation data mayindicate, for example, left turn data which may be representative of anumber of left turns a vehicle has navigated. One or more vehiclesensors (e.g., vibration sensors, light sensors or pressure sensors) maybe installed on the exterior of the vehicle, such as on the windshield.Sensor technology (e.g., sensor technology available from Nexense ETC)may be used to monitor the length of time a vehicle is in use. Nexense'ssensor technology may, for example, be used to measure sounds, movementand/or pressure within, and around, a vehicle. A pressure-sensitivesensor pad 123, 124 may be installed on a vehicle driver's seat. Datareceived from the vehicle driver's seat pressure sensor 123, 124 may beused to determine a length of time a driver's side seat was occupied.Alternatively, or additionally, a pressure sensor may be placed on anexterior of a vehicle (e.g., on the windshield). Data from the exteriorpressure-sensitive sensor may be used, for example, to measure air flowover the vehicle as the vehicle is in motion. In another example, anaudio sensor (e.g., a microphone 151, 152 of FIG. 1) may be used tomonitor engine sound as the vehicle is in use. Furthermore, empiricalvehicle operation data may be based on vehicle sensor data (e.g., seatoccupancy sensors 123, 124, seat belt sensors 128, 129, body heatsensors 141, 142, cameras 136, 137, etc.). Empirical vehicle operationrelated data may, for example, be representative of circumstances wheremultiple passengers traveling in an insured vehicle. Multiple passengersmay, for example, create a high risk for teenager vehicle operators. Asdiscussed in detail elsewhere herein, associated insurance policy ratemay be adjusted based on the empirical vehicle operation related data.

Empirical vehicle operation related data may be generated based on oneor more vehicle motion sensors (e.g., vibration sensors 107, 108,pressure sensors 123, 124 and/or a light sensors 136, 137). Data fromthe vehicle motion sensors may be time stamped and used to determine alength of time a vehicle was in use. Empirical vehicle operation relateddata may be transmitted to an insurance agency. The insurance agency maydetermine vehicle usage based on, for example, travel time data. Traveltime data may be used to determine vehicle insurance policy pricingadjustments and/or future policy payment adjustments for usage-basedvehicle insurance. Updated vehicle insurance policy information may beautomatically provided to an insurance customer.

Turning to FIGS. 7 and 8, a remote server 710 of a vehicle insurancepolicy data generation system 600 is depicted along with a method ofestablishing an insurance risk related data file on the server 800. Theremote server 710 may be similar to the remote server with insuranceapplication 110 of FIG. 1. The method 800 may be implemented byexecuting the modules 762-765 on a processor (e.g., processor 155 ofFIG. 1).

In any event, the remote server 710 may include an empirical vehicleoperator identity data receiving module 762, an empirical vehicleoperation data receiving module 763, a data correlation module 764 and avehicle insurance policy data generation module 765 stored on a memory760. The processor 155 may execute the empirical vehicle operatoridentity data receiving module 762 to cause the processor 155 to receiveempirical vehicle operator identity data (block 805). The processor 155may execute the empirical vehicle operation data receiving module 763 tocause the processor 155 to receive empirical vehicle operation data(block 810). The processor 155 may execute the data correlation module764 to cause the processor 155 to correlate at least a portion of theempirical vehicle operator identity data with at least a portion of theempirical vehicle operation data (block 815). The processor 155 mayexecute the vehicle insurance policy data generation module 765 to causethe processor 155 to generate vehicle insurance policy data based on thecorrelated empirical vehicle operator identity data and empiricalvehicle operation data (block 820). Alternatively, the processor 155 mayexecute the vehicle insurance policy data generation module 765 to causethe processor 155 to generate vehicle insurance policy data based on theempirical vehicle operator identity data and the empirical vehicleoperation data (block 820).

As a particular example of the generated insurance policy data, aninsurance policy may include a principle vehicle operator (e.g., personA having 0 recorded accidents). The principal vehicle operator may weigh125 lbs. A weight sensor 123, 124, positioned within a driver's seat ofan associated insured vehicle, may generate empirical vehicle operatoridentity data that indicates a person weighing 250 lbs. has operated thevehicle. For example, the empirical vehicle operator identity data mayindicate that a vehicle operator (e.g., person B having 10 recordedaccidents) has driven the insured vehicle most of the time.Alternatively, or additionally, data acquired from a facial recognitiondevice (e.g., a camera/image processor 136, 137) may be used to generateempirical vehicle operator identity data. The processor 155 may generatevehicle insurance policy data based on the empirical vehicle operatoridentity data. The processor 155 may transmit the vehicle insurancepolicy related data to an insurance underwriting agent for use incalculating a vehicle insurance rate. An insurance policy may beadjusted based on the vehicle insurance policy related data. Forexample, person B may be assigned as principle operator. Alternatively,the insurance policy may be adjusted based on a combination of person Aand person B. For example, a discount may be determined when a teenagerdrives solo 90% of the time. Alternatively, a vehicle insurance rate maybe increased when a teenager drives solo only 20% of the time. Theprocessor 155 may generate vehicle insurance policy data based onempirical vehicle operator identity data when underwriting householdcomposite vehicle insurance policies. The processor 155 may time stampthe empirical vehicle operator identity data. Thereby, the processor 155may determine an amount of time that a vehicle has been driven by aparticular individual based on the time-stamped empirical vehicleoperator identity data.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed is:
 1. A computer implemented method for automaticallygenerating insurance policy related data, the method comprising:identifying, by the one or more processors associated with a server, avehicle operator using vehicle operator identity data that is basedpartially upon vehicle sensor data, the vehicle operator identity dataidentifying one or more physical aspects of the vehicle operator andincluding an image of the vehicle operator; changing, by one moreprocessors associated with a vehicle module installed in a vehicle: astate of the vehicle to (i) prevent the vehicle operator from operatingthe vehicle when the determined identity of the vehicle operator doesnot match a vehicle operator who is insured to operate the vehicle, or(ii) allow the vehicle operator to operate the vehicle when thedetermined identity of the vehicle operator matches a vehicle operatorwho is insured to operate the vehicle; receiving, by the one or moreprocessors associated with the server, vehicle operation data that isbased partially upon the vehicle sensor data and is representative ofoperation of the vehicle by the vehicle operator who is allowed tooperate the vehicle; and calculating, by the one or more processorsassociated with the server, comprehensive vehicle insurance policyrelated data based upon a correlation between the vehicle operationdata, the vehicle operator identity data, and the vehicle operator suchthat the comprehensive vehicle insurance policy related data indicates(i) driving habits of the vehicle operator who is allowed to operate thevehicle, and (ii) a proportion of driving time in which the vehicleoperator was accompanied by one or more passengers when driving thevehicle.
 2. The method of claim 1, wherein the act of changing the stateof the vehicle to allow the vehicle operator to operate the vehiclecomprises: changing the state of the vehicle to allow the vehicleoperator to operate the vehicle when the vehicle operator matches avehicle operator that is insured to operate the vehicle based upon acomparison of the vehicle operator identity data to known vehicleoperator identity data.
 3. The method of claim 2, wherein the knownvehicle operator identity data is representative of (i) one or moreidentifiable physical aspects of vehicle operators that are insured tooperate the vehicle, and (ii) images of the vehicle operators that areinsured to operate the vehicle.
 4. The method of claim 1, wherein theact of changing the state of the vehicle to prevent the vehicle operatorfrom operating the vehicle comprises: identifying facial characteristicsof the vehicle operator to assess a physical status of the vehicleoperator in accordance with a predetermined set of rules; and changingthe state of the vehicle to prevent the vehicle operator from operatingthe vehicle when the vehicle operator is determined to be in a conditionunsuitable for operating the vehicle based upon the physical status ofthe vehicle operator.
 5. The method of claim 1, wherein the act ofcalculating the comprehensive vehicle insurance policy related datacomprises: calculating one or more insurance options includingunderwriting and pricing.
 6. The method of claim 1, wherein the vehiclesensor data is received from at least one vehicle sensor including oneor more of: a light sensor; a pressure sensor; a seat belt sensor; aseat occupancy sensor; an image sensor; a vehicle telematics systemsensor; a steering wheel angle sensor; a vibration sensor; a vehiclepitch sensor; facial recognition sensor; fingerprint sensor; eye scansensor; a vehicle yaw sensor; a vehicle speed sensor; a vehicle brakesensor; a steering wheel hand sensor; an air bag sensor; a microphone;an ultrasonic sensor; and an infrared sensor.
 7. A system forautomatically generating vehicle insurance policy related data, thesystem comprising: a server configured to: identify a vehicle operatorusing vehicle operator identity data that is based partially uponvehicle sensor data, the vehicle operator identity data identifying oneor more physical aspects of the vehicle operator and including an imageof the vehicle operator; and a vehicle module configured to: change astate of the vehicle to prevent the vehicle operator from operating thevehicle when the determined identity of the vehicle operator does notmatch a vehicle operator who is insured to operate the vehicle, orchange a state of the vehicle to allow the vehicle operator to operatethe vehicle when the determined identity of the vehicle operator matchesa vehicle operator who is insured to operate the vehicle, and whereinthe server is further configured to: receive vehicle operation data thatis based partially upon the vehicle sensor data and is representative ofoperation of the vehicle by the vehicle operator who is allowed tooperate the vehicle; and calculate comprehensive vehicle insurancepolicy related data based upon a correlation between the vehicleoperation data, the vehicle operator identity data, and the vehicleoperator such that the comprehensive vehicle insurance policy relateddata indicates (i) driving habits of the vehicle operator who is allowedto operate the vehicle, and (ii) a proportion of driving time in whichthe vehicle operator was accompanied by one or more passengers whendriving the vehicle.
 8. The system of claim 7, wherein: the vehiclemodule is further configured to change the state of the vehicle to allowthe vehicle operator to operate the vehicle when the vehicle operatormatches a vehicle operator that is insured to operate the vehicle basedupon a comparison of the vehicle operator identity data to known vehicleoperator identity data; and the known vehicle operator identity data isrepresentative of (i) one or more identifiable physical aspects ofvehicle operators that are insured to operate the vehicle, and (ii)images of the vehicle operators that are insured to operate the vehicle.9. The system of claim 8, wherein the server is further configured tocalculate the comprehensive vehicle insurance policy related data bycalculating one or more insurance options including underwriting andpricing.
 10. The system of claim 8, wherein the vehicle sensor data isreceived from at least one vehicle sensor including one or more of: alight sensor; a pressure sensor; a seat belt sensor; a seat occupancysensor; an image sensor; a vehicle telematics system sensor; a steeringwheel angle sensor; a vibration sensor; a vehicle pitch sensor; facialrecognition sensor; fingerprint sensor; eye scan sensor; a vehicle yawsensor; a vehicle speed sensor; a vehicle brake sensor; a steering wheelhand sensor; an air bag sensor; a microphone; an ultrasonic sensor; andan infrared sensor.
 11. The system of claim 7, wherein the vehiclemodule is further configured to change the state of the vehicle toprevent the vehicle operator from operating the vehicle when identifiedfacial characteristics of the vehicle operator used to assess a physicalstatus of the vehicle operator in accordance with a predetermined set ofrules indicate that the vehicle operator is in a condition unsuitablefor operating the vehicle.
 12. A non-transitory, tangible,computer-readable medium storing instructions that, when executed by aprocessor, cause the processor to: identify a vehicle operator usingvehicle operator identity data that is based partially upon vehiclesensor data, the vehicle operator identity data identifying one or morephysical aspects of the vehicle operator and including an image of thevehicle operator; change a state of the vehicle to prevent the vehicleoperator from operating the vehicle when the determined identity of thevehicle operator does not match a vehicle operator who is insured tooperate the vehicle, or change a state of the vehicle to allow thevehicle operator to operate the vehicle when the determined identity ofthe vehicle operator matches a vehicle operator who is insured tooperate the vehicle; receive vehicle operation data that is basedpartially upon the vehicle sensor data and is representative ofoperation of the vehicle by the vehicle operator who is allowed tooperate the vehicle; and calculate comprehensive vehicle insurancepolicy related data based upon a correlation between the vehicleoperation data, the vehicle operator identity data, and the vehicleoperator such that the comprehensive vehicle insurance policy relateddata indicates (i) driving habits of the vehicle operator who is allowedto operate the vehicle, and (ii) a proportion of driving time in whichthe vehicle operator was accompanied by one or more passengers whendriving the vehicle.
 13. The non-transitory, tangible, computer-readablemedium of claim 12, wherein the instructions to change the state of thevehicle to allow the vehicle operator to operate the vehicle furtherinclude instructions that, when executed by the processor, cause theprocessor to change the state of the vehicle to allow the vehicleoperator to operate the vehicle when the vehicle operator matches avehicle operator that is insured to operate the vehicle based upon acomparison of the vehicle operator identity data to known vehicleoperator identity data.
 14. The non-transitory, tangible,computer-readable medium of claim 13, wherein the known vehicle operatoridentity data is representative of (i) one or more identifiable physicalaspects of vehicle operators that are insured to operate the vehicle,and (ii) images of the vehicle operators that are insured to operate thevehicle.
 15. The non-transitory, tangible, computer-readable medium ofclaim 12, wherein the instructions change the state of the vehicle toprevent the vehicle operator from operating the vehicle further includeinstructions that, when executed by the processor, cause the processorto change the state of the vehicle to prevent the vehicle operator fromoperating the vehicle when identified facial characteristics of thevehicle operator used to assess a physical status of the vehicleoperator in accordance with a predetermined set of rules indicate thatthe vehicle operator is in a condition unsuitable for operating thevehicle.
 16. The non-transitory, tangible, computer-readable medium ofclaim 12, wherein the instructions to calculate the comprehensivevehicle insurance policy related data further include instructions that,when executed by the processor, cause the processor to calculate one ormore insurance options including underwriting and pricing.
 17. Thenon-transitory, tangible, computer-readable medium of claim 12, whereinthe vehicle sensor data is received from at least one vehicle sensorincluding one or more of: a light sensor; a pressure sensor; a seat beltsensor; a seat occupancy sensor; an image sensor; a vehicle telematicssystem sensor; a steering wheel angle sensor; a vibration sensor; avehicle pitch sensor; facial recognition sensor; fingerprint sensor; eyescan sensor; a vehicle yaw sensor; a vehicle speed sensor; a vehiclebrake sensor; a steering wheel hand sensor; an air bag sensor; amicrophone; an ultrasonic sensor; and an infrared sensor.